Management of configurations for existing storage infrastructure

ABSTRACT

The method for managing one or more standard configurations includes calculating a plurality of configuration fingerprints for a plurality of storage systems. The configuration fingerprint is a numerical value that represents information about a configuration for one or more components of a storage system. The method also includes building a result set that includes the plurality of configuration fingerprints for the plurality of storage systems. The method also includes identifying a plurality of standard configurations for the plurality of storage systems from the result set based on the plurality of configuration fingerprints. The method also includes determining that a first storage system from the plurality of storage systems meets a standard configuration from the plurality of standard configurations. The method also includes creating metadata within the first storage system that describes the standard configuration.

BACKGROUND

The present disclosure relates to storage devices, and morespecifically, to configuration identification within storage devices.

Storage systems are an important part of any computer system. Storagesystems may have one or more standard configurations which determine howthe storage system handles certain storage activities. These standardconfigurations can be identified so that the standard configurations canbe associated with any storage device that is present within a computersystem.

SUMMARY

According to embodiments of the present disclosure, a method, a system,and a computer program product are provided for managing one or morestandard configurations.

One embodiment provides for a method for managing one or more standardconfigurations. The method includes calculating a plurality ofconfiguration fingerprints for a plurality of storage systems. Theconfiguration fingerprint is a numerical value that representsinformation about a configuration for one or more components of astorage system. The method also includes building a result set thatincludes the plurality of configuration fingerprints for the pluralityof storage systems. The method also includes identifying a plurality ofstandard configurations for the plurality of storage systems from theresult set based on the plurality of configuration fingerprints. Themethod also includes determining that a first storage system from theplurality of storage systems meets a standard configuration from theplurality of standard configurations. The method also includes creatingmetadata within the first storage system that describes the standardconfiguration.

Another embodiment provides for a system for managing one or morestandard configurations. The system can include a plurality of storagesystems. The system also includes a memory and a processor devicecommunicatively coupled to the memory. The system also includes aconfiguration analyzer communicatively coupled to the memory and theprocessor device. The configuration analyzer is configured to calculatea plurality of configuration fingerprints for the plurality of storagesystems. The configuration fingerprint is a numerical value thatrepresents information about a configuration for one or more componentsof a storage system. The configuration analyzer is configured to build aresult set that includes the plurality of configuration fingerprints forthe plurality of storage systems. The configuration analyzer isconfigured to identify a plurality of standard configurations for theplurality of storage systems from the result set based on the pluralityof configuration fingerprints. The configuration analyzer is configuredto determine that a first storage system from the plurality of storagesystems meets a standard configuration from the plurality of standardconfigurations. The configuration analyzer is configured to createmetadata within the first storage system that describes the standardconfiguration.

Another embodiment provides for a computer program product for managingone or more standard configurations comprising a computer readablestorage device having a computer readable program stored therein,wherein the computer readable program, when executed on a computingdevice, causes the computing device to calculate a plurality ofconfiguration fingerprints for a plurality of storage systems, aconfiguration fingerprint is a numerical value that representsinformation about a configuration for one or more components of astorage system. The computing readable program causes the computingdevice to build a result set that includes the plurality ofconfiguration fingerprints for the plurality of storage systems. Thecomputing readable program causes the computing device to identify aplurality of standard configurations for the plurality of storagesystems from the result set based on the plurality of configurationfingerprints. The computing readable program causes the computing deviceto determine that a first storage system from the plurality of storagesystems meets a standard configuration from the plurality of standardconfigurations. The computing readable program causes the computingdevice to create metadata within the first storage system that describesthe standard configuration.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates a block diagram of a storage network managementsystem for analyzing a storage system and associating the storage systemwith a standard configuration, according to various embodiments.

FIG. 2 illustrates a flowchart of a method for managing classificationof a storage system, according to various embodiments.

FIG. 3 illustrates a flowchart of a method for determining whether astandard configuration applies to an unclassified storage system,according to various embodiments.

FIG. 4 illustrates a flowchart of a method for analyzing an environmentto identify used standards and associating standards with appropriatestorage systems, according to various embodiments.

FIG. 5 illustrates a flowchart of a method of identifying storagesystems that have a loose correlation to a standard configuration,according to various embodiments.

FIG. 6 illustrates various embodiments of a result set for the analysisof storage systems, according to various embodiments.

FIG. 7 illustrates a histogram representing CF values from a result set,according to various embodiments.

FIG. 8 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 9 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 10 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 11 illustrates a flowchart of a method for identifying standardswithin the result set, according to various embodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown in the drawings and will bedescribed in detail. It should be understood, however, that theintention is not to limit the invention to the particular embodimentsdescribed. On the contrary, the intention is to cover all modifications,equivalents, and alternatives falling within the spirit and scope of theinvention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to storage devices, moreparticular aspects relate to configuration identification within storagedevices. For example, aspects of the present disclosure relate toidentifying standard configurations of storage systems. Theidentification of the standard configuration begins by creating/buildinga result set with multiple configuration fingerprint values that matchesconfiguration fingerprint values to a storage system. Aspects of thepresent disclosure also relate to analyzing the result set to determinethe standard configuration of a storage system using the configurationfingerprints. If a storage system has an associated standardconfiguration, then the standard configuration can be written tometadata for the storage system. While the present disclosure is notnecessarily limited to such applications, various aspects of thedisclosure may be appreciated through a discussion of various examplesusing this context.

Cloud computing is becoming the dominant way of managing informationtechnology at the infrastructure level, independent of whether the cloudis a public cloud (i.e. resources are managed for usage outside of theproviding organization) or a private cloud (resources are managed andconsumed within an organization). In the private cloud paradigm, cloudcomputing will have significant impact on data centers owned bycompanies providing Information Technology (IT) services for their ownbusiness units, since cloud computing promises a lot of benefits for theconsumers of the IT services.

One of the key aspects making the benefits of cloud computing possibleis the ability to leverage a high degree of automation, andstandardization and categorization using metadata (which describes themanaged entities from the standardization perspective to allow for theconsistent and automated handling needed), to accelerate ITinfrastructure configuration changes, lower administrative burden, andenable IT services to be provided to end-users in a self-serve manner.Examples of this metadata include the standardized descriptions andassessments of infrastructure capabilities in performance, availability,accessibility, etc. The standardized descriptions also refer to standardconfigurations.

Independent of the cloud paradigm, storage administrators can aim formore simplistic standardizations independent of a cloud managementmodel, in order to deal with the complexity of managing a large numberof individual but related entities. Examples of such standardizationsare the usage of naming conventions, the dedication of certain storagesystems or storage pools to certain usages, or the usage of certainconfiguration options as a kind of template.

The management of storage environments may involve using manualprocesses that are planned in detail and initiated by the storageadministrators, although the process is sometimes supported by tools orscripts for certain operations. Some storage environments includedeviations from a standard configuration of a storage environmentbecause either the request was handled as a one-off, a storage devicewas not available at the time due to maintenance, or a template was notproperly applied. Another possible reason for deviations is the ongoingadjustment of standards/templates over time, e.g., to address lessonslearned, changed environment constraints, or new personnel.

The term standard as used in the present disclosure describes a certainconfiguration (i.e., a standard configuration) of a particular type ofmanaged storage system. There might exist multiple storage systems withthe same standard in a given storage environment to reflect differentcapabilities of and/or address different requirements for storagesystems. The standards represent reoccurring capabilities/requirementsusing for a larger number of managed entities to allow efficientmanagement of said entities, for example using automation. The termservice class and standard configuration is used interchangeably aswell.

Some storage environments are comprised of an often unmanageable numberof configurations, which may be a result of either matching a standardused by the organization (or a former version of the standard) byidentifying a configuration close to the standard with varying degreesof deviations, or the configuration may be individually configured forvarious reasons, e.g., one-offs etc.

In various embodiments of the disclosure, standards can be identifiedand all the managed storage systems compliant to the standard (includingthose following other standards which are similar to the standard) canassociate the necessary metadata in an automated fashion. Embodiments ofthe present disclosure may enable a larger number of organizations tobenefit from the advantages of cloud-like infrastructure management,e.g., as increased efficiency, faster reaction to storage requests, orimproved environment utilization.

By identifying a set of standard configurations for the managed entities(which can smaller than the total number of unique configurationspresent in most grown storage infrastructures), the management of themanaged entities can be automated and streamlined, resulting in lowerrequirements for manual administrator intervention. Managed entities notcurrently meeting any of the identified standard can be manually orautomatically transformed to be compliant with the standard beingclosest to their current configuration. Also, once associated with astandard configuration, the managed entity can be monitored for stayingin compliance with the associated standard. A managed entity is astorage system that is being managed by a storage resource managementserver. Throughout the disclosure, the term storage system can be usedinterchangeably with managed entity.

The usage of standards in the configuration of storage systemsenables/facilitates automation to improve efficiency in storagemanagement and allows the usage of the available storage resources bypotentially adjusting the standard definition (and hence theconfiguration of the managed entities associated with the standard) tooptimize with respect to resource consumption (like CPU usage, cacheusage, etc.) in the used storage infrastructure.

FIG. 1 illustrates a block diagram of a storage network managementsystem 100 for analyzing a storage system and associating the storagesystem with a standard configuration, according to various embodiments.The storage network management system 100 can have a storage resourcemanagement server 122. The storage resource management server 122 cancontrol the storage for a storage network management system 100. Aspectsmay include the transfer of files between storage volumes or differentcomputing systems, or the amount of a data that any particular storagedevice may hold at any time.

The storage resource management server 122 can be connected to arepository database 124. The repository database 124 may be acorresponding connected central repository to store all relevant systemand infrastructure data. In various embodiments, the repository database124 may also store a result set. The result set (described furtherherein) may be a database/table of all of the storage system identifiers(IDs) for the storage systems and their respective configurationfingerprints. The result set may contain more information about thespecific configuration that produced the configuration fingerprints. Theconfiguration fingerprint is a numerical value that representsinformation about a configuration for one or more components of astorage system. In various embodiments, the configuration fingerprint isa number indicating a specific type of configuration for a storagesystem. The configuration fingerprint may also be a specific grade orscore relating to the configuration, e.g., grade A high speed, grade Bmedium-speed, etc. The term configuration fingerprint values may be usedinterchangeably with configuration fingerprint throughout thisdisclosure.

The storage resource management server 122 can also interface with userinterface 120. The user interface 120 may be a graphical user interface(GUI) that visualizes required information for an end user. The userinterface 120 may also allow configuring and maintaining the system. Thestorage resource management server 122 can have a GUI backend 128 thatfacilitates communication between the user interface 120 and the storageresource management server 122.

The storage resource management server 122 may also include one or moredata collectors 126. The data collector 126 can be configured to gatherinformation from the storage network infrastructure via theirconnectivity to Storage Area Network (SAN) devices, e.g.,switches/fabrics 114, storage systems 116, or computer systems 110.

The storage resource management server 122 may also include aconfiguration analyzer 130. Various aspects of the configurationanalyzer 130 may relate to the present disclosure. For example, theconfiguration analyzer 130 may analyze an existing storage environmentfor used standards, identify the standards, and create the metadata withthe standard configuration for the managed storage systems.

The configuration analyzer 130 can analyze one or more storage systems116. In various embodiments a storage system 116 is a logical constructsuch as a volume. The volume may refer to a logical storage unit. Thestorage system 116 may also refer to a physical device, such as a harddrive. The configuration analyzer 130 can determine a ConfigurationFingerprint (CF) for a storage system 116. The configuration analyzer130 may also store additional information in the repository database 124of the storage resource management server 122 as needed. For example,the configuration analyzer 130 may store the CF values within therepository database 124.

The configuration analyzer 130 may also analyze the data from the resultset in the repository database 124. As a result of the analysis, managedentities to be inspected would either be categorized as being of aparticular standard (and have the corresponding metadata of the managedentity associated by writing the standard to the metadata), or marked asone-off configurations which require a storage administrator tocategorize them manually.

In case no significant accumulation of managed entities having the sameor close by CF values can be determined based on the analysis of themanaged environment, a manual classification/association between managedentities and manually defined standards may be necessary.

Storage systems (also referred to as managed storage entities) may referto all elements of the managed storage environment (e.g., the storagenetwork management system 100), which might be relevant for a cloud-likemanagement approach as discussed in this document, e.g., storagevolumes, file shares, or storage pools. The term volume can refer to aparticular type of managed entity used in block storage systems torepresent the logical storage unit presented to a consuming computer.The configuration analyzer 130 can be configured to associate anunclassified storage system with one of the standard configurationsfound within the result set.

The storage resource management server 122 can be configured to supporthighly automated provisioning of relevant entities, e.g., creatingstandardized configurations of the corresponding entities based ontemplate definitions or service classes (which can be parameterized),associating the necessary metadata (e.g., the used service classitself), as part of the automated provisioning. Based on this metadataassociation, other sophisticated management capabilities (e.g.optimization, data protection) can be enabled to be handled by the SRMserver 122.

In various embodiments, the storage network management system 100 can beconfigured to be hosted in a cloud-computing environment. For example,aspects of the storage network management system 100, e.g., therepository database 124, and the storage systems 116, can benefit fromimproved latency from a cloud-computing environment. In particular, ifthe configuration analyzer 130 of the SRM server 122 is hosted on thesame compute node as the repository database 124, and storage systems116, then the performance improvements can be more pronounced. Invarious embodiments, the cloud computing environment can also allow thestorage network management system 100 to rapidly scale additionalstorage systems 116 without significant infrastructure improvements.

FIG. 2 illustrates a flowchart of a method 200 for managingclassification of a storage system, according to various embodiments.The method 200 is a sample implementation of how a configurationanalyzer manages and determines a standard configuration for a storagesystem. More implementations are described further herein. Theconfiguration analyzer can be responsible for receiving the storagesystem to be classified and associating the storage system with anexisting standard configuration. A standard configuration can be basedon a distribution analysis of CF values of storage systems within theresult set. In various embodiments, once the configuration analyzerreceives an unclassified storage system, the configuration analyzer candetermine the CF value and determine if the CF value for theunclassified storage system falls within an existing standard. Themethod 200 may begin at operation 210.

In operation 210, the configuration analyzer may receive an unclassifiedstorage system. The unclassified storage system may be any storagesystem or any granularity therein that does not have a correspondingstandard configuration associated with the storage system. In variousembodiments, the unclassified storage system may be actively scanned bythe storage resource management server. The unclassified storage systemmay already be present in a result set without a corresponding standardassociated with the unclassified storage system. Once the status of theunclassified storage system is determined, then the storage resourcemanagement server may activate the configuration analyzer and the method200 continues to operation 212.

In operation 212, the configuration analyzer can determine aconfiguration fingerprint (CF) value for the unclassified storagesystem. The CF value can be determined using the metadata of theunclassified storage system. For example, the CF value can measurewhether features on the unclassified storage system are utilized withinthe storage device, e.g., thin provisioning or the copy service. Eachmetadata field can have a certain weight associated with the field whichis used to determine a CF value for the unclassified storage system. TheCF value may be assigned to each managed storage system, and thedetermination can be further described herein.

In various embodiments, the CF value can be determined by receiving oneor more configuration parameters for a managed storage system within amanaged storage environment. The managed storage environment can be thesame as the storage network management system and the managed storagesystem can refer to a classified or unclassified storage system. Theconfiguration parameters may be based on metadata categories. Forexample, thin provisioning may be a configuration parameter. The CFvalue can be further determined by the configuration analyzer bycalculating the configuration fingerprint value, based on theconfiguration parameters, for the managed storage system.

An aspect of the present disclosure involves determining theconfiguration fingerprint (CF) value. The CF value (which may also bereferred to as a CF) is like a hash value in that the CF valuecorresponding to the storage system may be used in a table. For example,the CF value in the table may point to a variety of the factors thatmake up the CF value. The CF value can represent a certain configurationof a storage system (which may also be referred to as a managed entity),e.g., a storage volume. The CF value can take the relevantcharacteristics of the managed entity into account. Examples of relevantcharacteristics may include, storage system type, underlying disktechnology, redundancy configuration, thin provisioning, encryption,compression, copy service configuration, naming conventions, oraffinity/collocation.

In various embodiments, an algorithm can produce configurationfingerprints which would indicate, by taking the difference between twocalculated CFs, by the magnitude by which the two configurations differ.That means that identical configurations would produce the same CF, anda configuration which differs only in a small aspect from anotherconfiguration would produce a CF very close to the other configuration'sCF. The algorithm could use corresponding weighting to control theimpact a given deviation from a configuration has on the difference inCF. Various embodiments may describe different methods of calculating aCF. These methods are described further herein. Once the configurationanalyzer determines a CF value for the unclassified storage system, thenthe method 200 continues to operation 214.

In operation 214, the configuration analyzer can access a result set ina repository database. In various embodiments, accessing the result setoccurs simultaneous with receiving the unclassified storage system. Theresult set can be a listing of various managed storage systems with thecorresponding CF values for each managed storage system. In variousembodiments, the result set can have a plurality of configurationfingerprints for a plurality of storage systems. The result set may bestored in local memory of the storage resource management servers or maybe stored in a repository database accessible to the storage resourcemanagement servers. The result set may be stored in any format, e.g., ina separate file, in a structured or unstructured database, etc. Once theresult set is accessed, then the method 200 can continue to operation216.

In operation 216, the configuration analyzer determines whether one ormore standard configurations apply to the unclassified storage system.The configuration analyzer may determine the standard configurations inthe result set based on the CF values. For example, if each storagesystem in a grouping of storage systems have a similar CF value to eachother, then each storage system within the grouping may be associatedwith a standard configuration. The configuration analyzer may alsocompare the CF value for the unclassified storage system (determined inoperation 212) to the CF values for the standard configurations in theresult set to obtain a standard configuration that can apply to theunclassified storage system. If a standard configuration does not apply,then the method 200 can continue to operation 218. If a standardconfiguration does apply, then the method 200 can continue to operation220.

In operation 218, the configuration analyzer can update a result setwith a new CF value for the unclassified storage system. The updatingcan occur by associating a new CF value with the unclassified storagesystem. In various embodiments, the configuration analyzer can create anew result set if there is not a result set accessible to the storageresource management servers. The result set can be updated by storingthe configuration fingerprint value associated with the unclassifiedstorage system in the result set. The CF value can be uploaded into thestorage system accessible by the storage resource management servers.

In operation 220, the configuration analyzer can associate anunclassified entity with the standard configuration in response to astandard configuration applying to the unclassified storage system. Forexample, if a standard configuration applies to the unclassified entity,then the standard configuration can be noted by the configurationanalyzer and stored along with data for the unclassified storage system.In various embodiments, the association can occur in a table, e.g., astructured database or the result set.

In various embodiments, managed storage systems not currently meetingany of the identified standards can be manually or automaticallytransformed to be compliant with the standard being closest to thestorage system's current configuration. Also, once associated with astandard configuration, the managed entity can be monitored for stayingin compliance with the associated standard.

The usage of standards in the configuration of storage systemsenables/facilitates automation to improve efficiency in storagemanagement and allows the optimal usage of the available storageresources by potentially adjusting a definition of a standardconfiguration (and the standard configuration of the managed storagesystems associated with the standard configuration) to reduce resourceconsumption (like CPU usage, cache usage, etc.) in the used storageinfrastructure.

In operation 210 thru operation 220, the unclassified storage system isassociated with a standard configuration. In operation 222 thruoperation 228, the storage systems within the result set (including thenewly associated unclassified storage system) have associated standardconfigurations enforced. In various embodiments, the enforcement of thestandard configurations can be optional depending on various systemconfigurations.

In operation 222, the configuration analyzer can monitor a storagesystem associated with a standard configuration for a change in one ormore configuration parameters. For example, if the storage system hasthree configuration parameters of Thin provisioning, Disk technology,and Copy Service, then a change is any configuration parameter can alertthe configuration analyzer. In operation 224, if a change is detected,then the method 200 can continue to operation 226.

In operation 226, the configuration analyzer can determine whether thechange modifies a configuration fingerprint of the storage system beyonda change threshold. The change threshold can represent the degree ofpermissible change for a configuration fingerprint. For example, if thechange threshold is plus or minus 5%, then a 6% change in the CF valuewould modify the value of the storage system beyond the changethreshold. If the change modifies the configuration fingerprint of thestorage system beyond the change threshold, then the method 200 cancontinue to operation 228.

In operation 228, the configuration analyzer can adjust, in response todetermining that the change modifies the configuration fingerprint ofthe storage system beyond the change threshold, one or moreconfiguration parameters of the storage system. For example, if astorage system has three configuration parameters: Thin provisioning,Disk Technology, and Copy Service with a change in the thin provisioningfrom off to on, then the configuration analyzer can change the CopyService to a different type in order for the storage system to have a CFvalue within the change threshold. The result of the adjustment may bereevaluated.

FIG. 3 illustrates a flowchart of a method 300 for determining whether astandard configuration applies to an unclassified storage system,according to various embodiments. The method 300 can correspond tooperation 216 in FIG. 2. The method 300 can involve identifying one ormore standard configurations within the result set, selecting a standardconfiguration and determining if the CF value for the unclassifiedstorage system is within the range of the standard configuration. Themethod 300 begins at operation 310.

In operation 310, the configuration analyzer can identify one or morestandard configurations within the result set for the plurality ofstorage systems. Standard configurations may be determined throughanalysis of the result set. For example, the CF values may be utilizedfor each storage system. A distribution of the CF values may be analyzedthrough various statistical means, e.g., a histogram, linear regression,normalized distribution, etc. The distribution can indicate the variousconfigurations of the storage systems that are related and represent astandard configuration for multiple storage systems. The distributionanalysis may be described further herein. Once the standardconfigurations are determined by the configuration analyzer, then themethod 300 continues to operation 312.

In operation 312, the configuration analyzer selects a standardconfiguration from the one or more standard configurations within theresult set. The configuration analyzer may select the standardconfiguration to compare to the configuration for the unclassifiedstorage system. In various embodiments, the unclassified storage systemmay be evaluated through other means, e.g., comparison, grouping, orpriority. The standard configuration may be selected using a variety ofmethods, e.g., random or in a pre-defined order. Once the configurationanalyzer selects a standard configuration, then the method 300 continuesto operation 314.

In operation 314, the configuration analyzer may determine whether astandard configuration is associated with the unclassified storagesystem. The unclassified storage system may have a standard that isdesignated by the configuration analyzer. For example, there may be astandard configuration A which is used by default on all hard driveswithin the volume B. There may also be a standard configuration that wasdetermined in previous result sets. For example, if, in a previousanalysis of the result set, a storage system is associated with astandard configuration the storage system was classified. If the storagesystem is received, the method 300 halts because the storage system isalready associated with a standard configuration. If the storage systemis not classified, then the method 300 continues to operation 316.

In operation 316, the configuration analyzer can access theconfiguration fingerprint for the unclassified storage system inresponse to the standard configuration not being associated with theunclassified storage system. The configuration analyzer can access theCF value for the unclassified storage system by receiving the determinedCF value from another source. In various embodiments, the CF value canbe determined external to the configuration analyzer and may be receivedby the configuration analyzer. The CF value can be determined in prioroperations. In various embodiments, the CF value can be determined bythe configuration analyzer and then stored in memory for laterretrieval. Once the configuration analyzer accesses the CF value for theunclassified storage system, then the method 300 can continue tooperation 318.

In operation 318, the configuration analyzer can determine whether athreshold for the standard configuration is met by the configurationfingerprint for the unclassified storage system. The threshold can be arange of CF values that conform to a particular standard configuration.For example, if the threshold is a range of CF values from 500-550 andthe CF value for the unclassified storage system is 525, then thethreshold is met. The threshold can be described further herein. If thethreshold for the standard configuration is met by the unclassifiedstorage system, then the method 300 can continue. In variousembodiments, the configuration analyzer can associate the unclassifiedstorage system with the standard configuration in response to theconfiguration fingerprint for the unclassified storage system meetingthe threshold. For example, the metadata of the unclassified storagesystem may be modified to refer to the standard configuration or anassociation table between one or more storage systems and the standardconfigurations may be updated. The association may be similar to theassociation of the unclassified storage system with the standardconfiguration described in operation 220 in FIG. 2.

If the threshold is not met, then the method 300 can continue tooperation 312. In various embodiments, the configuration analyzer canselect a second standard configuration (different than a first standardconfiguration) in operation 312. The configuration analyzer can evaluatewhether a second threshold for the second standard configuration is metby the configuration fingerprint for the unclassified storage system.

FIG. 4 illustrates a flowchart of a method 400 for analyzing anenvironment to identify used standards and associating standards withappropriate storage systems, according to various embodiments. Themethod 400 may be a sample implementation of identifying the standardconfigurations within a result set and associating the standard with thestorage system by writing the standard configuration in the metadata.Many of the operations in the method 200 may correspond to operations inthe method 400. The method 400 may begin at operation 410.

In various embodiments, operation 410 thru operation 418 may be directedto building a result set from the storage systems. Operation 420 thruoperation 428 may be directed toward identifying standards in the resultset. From this, the identified standard associated is associated with astorage system.

In operation 410, the configuration analyzer can retrieve data for thenext storage system. In various embodiments, the storage system can besynonymous with a managed entity and include a logical unit, e.g., avolume, or refer to a file share service. The data can includeinformation regarding the configuration of the storage system ormetadata regarding the standards associated with the storage system. Invarious embodiments, the storage system may have one or more components.The one or more components may point to a general configuration. Eachcomponent may have one or more properties or configuration parametersthat describe the configuration. The configuration parameters may have avalue. For example, if the component of the volume is a whether thevolume uses a particular disk technology, then the configurationparameter is a Solid State Disk (SSD). The SSD configuration parameterwill have a value of true or false. Once the configuration analyzerretrieves the data, then the method 400 continues to operation 412.

In operation 412, the configuration analyzer determines whether thestorage system has a standard previously associated. Operation 412 maybe similar to operation 314 in FIG. 3. If the storage system has astandard associated, then aspects of the method 400 do not apply andanother storage system is selected.

In operation 414, the configuration analyzer can calculate theconfiguration fingerprint. The calculation of the CF may occur using avariety of methods and depends on the components of the storage systemused. Generally, the calculation of the CF involves assigning a value toa configuration parameter that describes information about aconfiguration for the component, weighting the value for theconfiguration parameter; and aggregating a plurality of weighted valuesfor a plurality of components. The building/calculating of the CF valuemay utilize the data retrieved in operation 410 and discussed furtherherein.

In operation 416, the configuration analyzer can add (write) a CF andentity ID to a result set that may be stored. As mentioned herein, theresult set may be a repository or listing of all of the classifiedstorage systems along with their respective CF values. The entity ID cancorrespond to a specific storage system so that the storage system canbe readily identified. In various embodiments, the configurationanalyzer can build a result set that includes the plurality ofconfiguration fingerprints for the plurality of storage systems byadding. The building also includes accessing the plurality of storagesystems, calculating a configuration fingerprint for each storage systemfrom the plurality of storage systems, and adding the configurationfingerprint to the result set.

In operation 418, the configuration analyzer can determine if there aremore storage systems to analyze. If there are more storage systems toanalyze, then the method 400 can continue to select new storage systemsto add to the result set. In operation 420, the configuration analyzercan determine the distribution of configuration fingerprints. Thedistribution of the CFs can be determined by a statistical analysis,such as a histogram of the CFs and volumes with CFs and describedfurther herein.

In operation 422, the configuration analyzer identifiesstandards/service classes based on the CFs. The determination of thedistribution of CFs in operation 420 is related to the identification ofthe standards in operation 422. For example, once the distribution of CFvalues is determined, then the standards can be identified based on thedistribution. For example, if the distribution of CF values showclustering around a particular CF value, then the cluster may likely bea standard configuration. The determination of distributions andidentification of the standards can be further described herein.

In operation 424, the configuration analyzer can obtain the next entryfrom the result set. In various embodiments, the next entry may have aCF value and entity ID, but no standard configuration associated.Operation 424 may correspond to operation 210 in FIG. 2, according tovarious embodiments. After the configuration analyzer gets the nextentry from the result set, then the method 400 continues to operation426.

In operation 426, the configuration analyzer calculates a distancebetween the CF value for the unclassified storage system and the rangeof acceptable CF values for a standard configuration. In variousembodiments, the CF distance can be determined by taking the differencebetween the CF value and the mean or median CF value for a standardconfiguration. For example, if the median CF value for a standardconfiguration is 300 and the CF value for an unclassified storage systemis 325, then the CF distance would be 25.

In operation 428, the configuration analyzer can determine if the CFvalue for the unclassified storage system is within a threshold. Forexample, if the CF value for the unclassified storage system is 240 andthe range for the standard configuration is 275 to 325, then the CFvalue for the unclassified storage system would not be within thethreshold. If the threshold is not met, then the method 400 can continueto operation 430.

In operation 430, the configuration analyzer can create metadata withinthe first storage system that describes the standard configuration. Invarious embodiments, the configuration analyzer can associate theclosest standard with the storage system in response to the thresholdnot being met by the standard configuration. The closest standard (e.g.,a second standard configuration) may be determined relative to the CFdistance to the range of the standard configuration. For example, if theCF distance from the unclassified storage system to a first standardconfiguration is 300 but the CF distance from the unclassified storagesystem to a second standard configuration is 200, then the secondstandard configuration may be the closest.

The CF distance may be measured in an alternative manner based upon theCF distance being a range of CF values for a standard configurationwithin a result set. According to various embodiments, in operation 426,the CF distance may also be a range of CF values within a standardconfiguration. For example, the CF distance can be the range of CFvalues that are associated with a particular standard configuration. Astandard configuration may also have a CF distance threshold. The CFdistance threshold sets the permissible difference from a low CF valueto a high CF value for a grouping of CF values to be associated with astandard configuration.

In operation 428, the configuration analyzer can determine whether theCF distance threshold is met for a particular standard. For example, ifa CF distance between a storage system with a low CF value and a storagesystem with a high CF value is 100 but a distance threshold is 75, thenthe distance between two storage systems is greater than the thresholdand is thus not met. If the threshold is not met, the cluster of CFvalues is not sufficient to become a standard. Each value from thecluster of CF values can be associated with the closest (established)standard in operation 430. For example, if a cluster of CF values,including the unclassified storage system, does not meet a threshold fora standard configuration, then each storage system within the clustercan be associated with the closest standard configuration as definedherein. Once a standard configuration is associated with theunclassified storage system, then the method 400 continues to operation432.

In operation 432, the configuration analyzer can determine whether thereare more storage systems to analyze. For example, if there are moreunclassified storage system within the result set, then the method 400can continue to operation 424. Otherwise, the method 400 continues toreference A.

FIG. 5 illustrates a flowchart of a method 500 of identifying storagesystems that have a loose correlation to a standard configuration,according to various embodiments. The method 500 can include checkingthe storage systems against additional conditions, and determiningwhether the entities differ, and marking the storage systems aspotential false-positive/false-negative. The method 500 may continuefrom reference A in FIG. 4. The method 500 begins at operation 534.

In operation 534, the user can determine whether to perform a falsepositive (F-P) and false negative (F-N) validation. For example, thetype of applications using the volume could be used to check similarityfrom an additional point of view. If, based on the CF analysis, a set ofvolumes may be determined to be the same, then a false positiveclassification might be designated when almost all volumes are used by aparticular type of application and only a few are used by a differenttype of application. The false positive and false negative can bedetermined in a variety of manners. For example, in a false positivesituation where 50 storage volumes use a standard configuration of adatabase application, 49 storage volumes can be used by a database app,while 1 storage volume used by a web streaming app could be erroneouslyassigned to the standard configuration of the database application. In afalse negative situation, the standard configuration for a databaseapplication may not be associated with a storage system but variousindications indicate that the standard configuration should be used. Theconfiguration analyzer can validate the false-positive, false-negativeby evaluating the result set against a history of user changes.

In operation 536, the configuration analyzer checks all storage systemsagainst additional conditions. The additional conditions may concernproperties that are not considered in the CF. For example, if the CFconsiders volume size, thin provisioning, and compression, but notencryption, then the additional consideration may include encryption.The encryption may be weighted in a different manner than the other CFfactors. Detecting the F-P/F-N may include the presence or absence of anadditional factor. For example, in a F-P situation where the a storagesystem is classified wrongly in a first standard, the presence of anadditional conditions (e.g., encryption) may help distinguish thestorage system from other storage systems in the first standard andassociate the storage system to a second standard that uses theadditional conditions.

In operation 538, the configuration analyzer can determine if thestorage systems differ when additional conditions are considered. Forexample, for a first storage system and a second storage systemassociated with a first standard where an additional condition ofencryption is considered, the second storage system may no longer workwith the first standard and the two storage systems differ. If thestorage systems differ, then the method 500 continues to operation 540.If the storage systems do not differ (e.g., the first storage system andsecond storage system still correspond to the first standard after theadditional condition is considered), then the method 500 may halt.

In operation 540, the configuration analyzer can mark the storagesystems that differ as potential F-P/F-N. The marking can occur at atable for the result set. For example, in addition to the standardconfiguration and the entity ID, the table may also have informationrelated to whether the standard configuration of the entity ID is also apotential false positive or potential false negative. The configurationanalyzer can alert a user that the storage system is a potential F-P/F-Nand encourage the user to take further action. In various embodiments, astorage system that is marked as a potential F-P/F-N can be removed fromassociation with the standard configuration.

Referring now to FIG. 11, FIG. 11 illustrates a flowchart of a method1100 for identifying standards within the result set, according tovarious embodiments. The method 1100 can correspond to operation 422 inFIG. 4. The method 1100 can involve calculating a combined number of allCF values within the CF distance threshold and ensuring that thecombined numbers are within various thresholds before determining that aCF value is within a standard configuration. The method 1100 may beginat operation 1110.

In operation 1110, the configuration analyzer can retrieve the CF valuefor a next storage system. In operation 1112, the configuration analyzercan calculate the combined number (i.e., value) of all CF values withina CF distance threshold. For example, the CF distance threshold can be arange of CF values that would create a standard configuration. The sumor combined number can be determined by taking the aggregate number ofstorage systems with the same CF value. For example, if there are ifthere are three CF values with a value of one-hundred, and two CF valueswith a value of one-hundred one within the CF distance threshold, thenthe combined CF value would be three and two respectively. In variousembodiments, the combined numbers can also be determined using the totalvalue of all CF values within the CF distance threshold. For example, ifthere are three CF values with a value of one-hundred, and two CF valueswith a value of one-hundred one within the CF distance threshold, thenthe combined CF value would be five-hundred two.

In operation 1114, the combined number is then compared to a combinednumber threshold. The combined number threshold may represent a minimumnumber of CF values to become a standard configuration, according tovarious embodiments. In various embodiments, the number of storagesystems having a particular CF value, once exceeded, would create asufficient data set to generate a standard configuration. If thecombined number of CF values is greater than the combined numberthreshold, then the method 1100 can continue to operation 1116.

In operation 1116, the configuration analyzer can check for adistribution peak. The distribution peak may represent the CF valueassociated with a number of storage systems. In various embodiments, thedistribution peak may represent a local maximum, i.e., a set ofclustered CF values where there is a greater number of storage systemswith a CF value than the next similar CF value. For example, if a firstCF value has 20 storage systems and a second similar CF value (within acombined number threshold) has 10 storage systems, then there may be adistribution peak present. If the distribution peak is present, then themethod 1100 can continue to operation 1120. If the distribution peak isnot present, e.g., when a first CF value and a second CF value have anequal number of storage systems associated, then the method 1100 cancontinue to operation 1110. In operation 1120, the configurationanalyzer can define the configuration of the CF values within thecombined number threshold, i.e., the cluster, as the standardconfiguration. In various embodiments, defining is similar toassociating the standard configuration with the storage systems havingthe CF values.

FIG. 6 illustrates embodiments of a result set for the analysis ofstorage systems, according to various embodiments. The embodiments caninclude table 600 and table 610 which may each use a different algorithmto determine a CF value. The following table 600 provides a list ofdifferent configuration, along with the resulting CF values. Thedifference in CF values between two volumes increases with thesignificance of the configuration differences between the volumes. Forexample, if volume A used EasyTier™ instead of Solid State Disk (SSD) asa disk technology, and no copy service, then the CF value of volume Awould change. In various embodiments, a volume is created according tostandards. Multiple standards can apply to the same volume. If thestandard subsequently changes, then the volume may be reevaluated. Ifthere are changes to storage practices, then the weighing may adjust tocompensate. Various aspects may include using a bit field approach tostore metadata, where bits of series of bits are used to determine thestate of a particular attribute associated with the bit field.

In various embodiments, the table 600 may be created by theconfiguration analyzer independent of determining the CF value for anunclassified storage system. The table 600 may be modified in operation416 in FIG. 4. The table 600 may also be accessed in operation 214 inFIG. 2 and operation 424 in FIG. 4.

An example of an algorithm using a bit field approach is provided intable 600. For example, Each characteristic to be considered for the CFof a given managed entity is assigned a specific bit in a bit vector,e.g., bit 0: Thin provisioning: Off (20=1); bit 1: Thin provisioning: On(2¹=2); bit 2: Disk technology: SATA (2²=4); bit 3: Disk technology: SAS(2³=8); bit 4: Disk technology: EasyTier (2⁴=16); bit 5: Disktechnology: SSD (2⁵=32); bit 6: Copy services used: None (2⁶=64); bit 7:Copy services used: GM (2⁷=128); bit 8: Copy services used: MGM(2⁸=256); bit 9: Copy services used: MM (2⁹=512).

One important aspect of this sample algorithm is the non-linear increasein weight of each individual value of the chosen characteristics. Thevalues need to be assigned to each of the bit position in such a waythat the more the influence they have on the difference between managedentities with and without this value, the greater its resulting value isin the sample algorithm described here. In various embodiments, thesample algorithm includes increasing a first value to a second value,non-linearly, between configuration parameters.

In this example, the CF of a given managed entity is calculated bysetting the bits in the bitfield corresponding to the characteristics ofthe given managed entity, and then simply using the resulting decimalvalue as the managed entity's CF.

In table 600, volumes Vol_B and Vol_D differ only slightly in the disktechnology used (EasyTier vs. SAS), so the difference between their CFsis relatively small, being an indication that a single standard couldpotentially be defined for both volumes. Then, one of these volumescould be transformed (e.g. migrated to a different storage pool) tofulfill the disk technology prescribed by the standard.

In the algorithm, the significance of the individual settings can beadjusted by shifting the position either left or right in the bit-fieldto either increase or decrease the significance for the fingerprintvalue. In addition, it may be possible to adjust the inclination ofdifferent values by combining them into fewer bits (for example, byusing just 2 bits to represent the four possible values for disktechnology instead of 4 bits). In various embodiments, an algorithmusing prime numbers instead of the bit values might be used to calculatethe CF or any other mechanisms satisfying the constraints of thefingerprint algorithm.

In the table 600, the volume name may refer to the storage system. Thedisk technology, thin provisioning, copy service can refer tocomponents. The types of disk technology, e.g., SSD, EasyTier, SAS,SATA, may all refer to types of configuration parameters. Eachconfiguration parameter may have one or more values that representvarious options of the configuration parameter.

In table 610, a result set embodiment featuring a second example of analgorithm is provided. For example, a configuration analyzer canconsider the storage system's configuration parameters of Disktechnology, Thin provisioning, and Copy service configuration. Theconfiguration parameter values could resemble the following: Disktechnology: SSD: 1, EasyTier: 2, SAS: 3, SATA: 4; Thin provisioning:Off: 1, On: 2; Copy service configuration: MetroMirror: 1,MetroGlobalMirror: 2, GlobalMirror: 3, None: 4.

The configuration parameter which is to make the most difference in theCF calculation in this example is the copy service configuration,followed by disk technology, and then by thin provisioning. Thecorresponding weighting factors thus are the following: Disk technology:20, Thin provisioning: 10, Copy Service Configuration: 30. In thisexample, the CF is calculated by multiplying the configuration parametervalue with the corresponding weighting factor, and then summing up theresulting products across all CF parameters considered.

FIG. 7 illustrates a histogram 700 representing CF values from a resultset, according to various embodiments. The histogram 700 can be used todetermine the distribution of CF values for a plurality of storagesystems (and entity IDs). The histogram 700 can have one or moreclusters of CF values (e.g., 710, 712, 714, 716). Each cluster may besimilar in CF value and may correspond to a standard configuration. Fourpotential clusters (e.g., 710, 712, 714, and 716) are shown in thehistogram.

For the used standard configurations, a large number of configurationfingerprint values being either equal or only differing in few aspectswould be expected. As shown in the histogram 700, the standardconfigurations would show up as groupings/clusters of CF values with ahigh number of repeat CF values. The CF values would have a CF frequencythat indicates how many times a CF value appears for the plurality ofstorage systems. One-off configurations (e.g., cluster 714) would have asmall quantity of storage systems with similar CF values using thestandard configuration.

An identification algorithm using thresholds and CF distance measuringcan be used to find multiple standard configurations using thresholds(e.g., 722) and CF distance boundaries (e.g., 714) in order to beadaptable to the analyzed environment.

For example, in cluster 710, there may be multiple storage systems withthe same CF. Each CF value may be represented by A, B, C, D, E, and Fwith the frequency of CF values determining the height. The range or CFdistance may be determined. The CF distance extends from E to F. Thecluster 710 may have a certain number of values to be considered 720.The certain number establishes a baseline for the sample size to beconsidered a data point. In this example, A, B, and C meet the minimum720. The CF value A would also be considered a distribution peak. Invarious embodiments, the minimum number 720 consideration may beoptional.

A CF distance threshold may 722 also be applied. Any CF values outsideof the threshold may not be considered part of the standardconfiguration. For example, D, E, and F do not meet the threshold 722 orthe minimum 720 and would not be considered part of the standardconfiguration. However, D, E, and F are close enough to the cluster 710and the corresponding standard configuration that the storage systemscorresponding to D, E, and F may be considered associated with thestandard configuration.

Applying the analysis to the other clusters (e.g., 712, 714, and 716),only 712, and 716 would be considered standards based on the CFthreshold 722 used. For example, 712 and 716 meet the minimum number ofsamples 720 threshold and have little variance (CF distance). Cluster714 would not be a standard configuration because the minimum number ofsamples was not reached.

In different embodiments of the present disclosure, the identificationof the standards to be used could occur through manual selection, by astorage administrator, of managed entities known to be compliant withthe standard. The identification of standards to be used could alsooccur based on CF values of the configurations of previously definedstandard configurations that define the standard configurations tocompare with. For example, if a storage system has a CF value of 400,and a first standard configuration has a permissible range of 344-422and a second standard configuration has a permissible range of 222-955,then the storage system can be compared against the first standardconfiguration and the second standard configuration.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 8, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 8, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 9, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 10, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 9) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 3 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and configuration analysis. For example, the configurationanalysis may be performed by the configuration analyzer within a storageresource management server. The configuration analysis may includeevaluating one or more storage systems for standard configurations usedby the storage system and matching an unclassified storage system with astandard configuration.

Referring to FIGS. 1-11:

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for identifying a standardconfiguration, comprising: calculating a plurality of configurationfingerprints for a plurality of storage systems, wherein a configurationfingerprint is a numerical value that represents information about aconfiguration for one or more components of a storage system; building aresult set that includes the plurality of configuration fingerprints forthe plurality of storage systems, wherein the building comprises:accessing the plurality of storage systems; matching a configurationfingerprint, from the plurality of configuration fingerprints, with astorage system from the plurality of storage systems; and adding theconfiguration fingerprint to the result set; identifying a plurality ofstandard configurations for the plurality of storage systems from theresult set based on the plurality of configuration fingerprints, whereinthe identifying comprises: calculating a configuration fingerprintdistance for a cluster of configuration fingerprints within the resultset; determining whether a configuration fingerprint distance thresholdis met by the configuration fingerprint distance; calculating a combinednumber for the plurality of configuration fingerprints within theconfiguration fingerprint distance threshold; determining whether acombined number threshold that describes a number of storage systems fora configuration fingerprint is met; determining whether a distributionpeak for one or more configuration fingerprints is present; defining, inresponse to the distribution peak being present and the combined numberthreshold being met, the standard configuration for the storage systemin the plurality of configuration fingerprints; and writing, in responseto the configuration fingerprint distance meeting the configurationfingerprint distance threshold and to the defining, the standardconfiguration to metadata of one or more storage systems within thecluster of configuration fingerprints; determining that a first storagesystem from the plurality of storage systems meets a first standardconfiguration from the plurality of standard configurations; andcreating metadata within the first storage system that describes thefirst standard configuration.
 2. The method of claim 1, wherein astorage system includes a volume that represents a logical storage unit.3. The method of claim 1, wherein calculating a configurationfingerprint includes: assigning a value to a configuration parameterthat describes information about a configuration for the component;weighting the value for the configuration parameter; and aggregating aplurality of weighted values for a plurality of components.
 4. Themethod of claim 3, wherein weighting the value includes increasing afirst value to a second value, non-linearly, between configurationparameters.
 5. The method of claim 1, wherein determining that the firststorage system meets the first standard configuration includes:comparing the configuration fingerprint for the first storage system toa configuration fingerprint threshold for the first standardconfiguration; determining whether the configuration fingerprint for thefirst storage system meets the configuration fingerprint threshold; andidentifying, in response to determining that the configurationfingerprint meets the configuration fingerprint threshold, theconfiguration fingerprint for the first storage system as meeting thefirst standard configuration.
 6. The method of claim 1, furthercomprising: checking the first storage system and a second storagesystem associated with the first standard configuration in the resultset against one or more conditions; determining whether the firststorage system and the second storage system differ with regard to theone or more conditions; and marking the second storage system as apotential false positive for the first standard configuration.
 7. Themethod of claim 1, further comprising: monitoring a storage systemassociated with the first standard configuration for a change in a valuefor one or more configuration parameters; determining that the change inthe value modifies a configuration fingerprint of the storage systembeyond a change threshold; and adjusting one or more configurationparameters of the storage system.